UVLM v4.0.0 — Gemma 4, the Transformers 5 Migration, and Why This One Is a Major Version
Highlights New model family: Gemma 4 (Google [...]
UVLM v3.2.0 — InternVL3.5 Joins the Registry, With Zero Notebook Changes
UVLM v3.2.0 adds InternVL3.5 (1B–38B, six checkpoints): 21 open VLM checkpoints across 4 families, one Python interface. The new family appeared in the notebooks without a single notebook edit — plus per-model output files for cleaner benchmarking.
UVLM v3.1.0 — Qwen3-VL Joins the Registry, With Family-Based Model Selection
UVLM v3.1.0 adds a third model family, Qwen3-VL (2B–32B Instruct), bringing the registry to 15 checkpoints. The notebooks gain a two-level family/model selector, the loader picks BF16 automatically on capable GPUs, and the smallest new model runs in about 2 GB of VRAM. Same three-block workflow, same prompts, one more family to compare.
Deploy Your Own Local LLM on Low VRAM in 30 Minutes — A Private Chat Assistant in Jupyter
Run a capable large language model entirely on your own machine — private, offline, and with as little as 8 GB of GPU memory. This hands-on guide sets up a clean Python environment, gets CUDA working even on the newest NVIDIA Blackwell cards, loads a 4-bit quantized model from Hugging Face, and builds an interactive chat widget with conversation memory and a live VRAM gauge in JupyterLab. No cloud, no API keys, no data leaving your computer.
SAGAI v2.0 — A Unified Multi-Model Notebook for Streetscape Analysis
SAGAI v2.0 consolidates the full streetscape analysis pipeline into a single Google Colab notebook and replaces the inline LLaVA-only inference code with the UVLM package, enabling multi-model benchmarking across 11 VLM checkpoints. New features include a multi-task prompt builder, consensus validation with majority voting, chain-of-thought reasoning, truncation detection, interactive Folium maps, view-direction filtering, and support for loading existing polygons as study area boundaries.
UVLM v3.0.0: From Colab Notebook to Python Package — Run Vision-Language Models Anywhere
UVLM v3.0.0 turns a Colab notebook into a full Python package. Run vision-language models locally, in notebooks, or scripts with a simple API and no setup complexity.
From Large Language Models to Autonomous AI Agents — Architecture, Capabilities, and Emerging Risks
Large Language Models are stateless, single-pass prediction engines — powerful but passive. Wrapping them in a perception–action loop with environment access and tool use transforms them into something qualitatively different: autonomous AI agents. This post walks through the transformer architecture, explains how the agent paradigm introduces closed-loop reasoning over environments and tasks, surveys the growing toolkit ecosystem, and examines the emerging risk landscape.
Qwen Image Edit for Urbanism v1.3 — Mask-Controlled Editing With Prompt or Reference Guidance
Version 1.3 of Qwen Image Edit for Urbanism introduces mask-controlled editing in ComfyUI, enabling precise, localized image transformations using prompts or reference images. The new Grow Mask utility softens boundaries, preserves unmasked areas, and integrates seamlessly with existing single-image and sequential workflows.
Qwen Image Edit for Urbanism v1.2 — Custom Nodes & Sequential Processing
ComfyUI Sequential Image Editing for Urbanism arrives in Qwen v1.2 with custom Python nodes, multi-image batch processing, and a six-slot buffer for reproducible urban edits. This version streamlines automated workflows for researchers, designers, and architects working with street and neighborhood imagery.
Qwen Image Edit for Urbanism v1.1 — Editing using a Reference Image and Advanced Sampling
Qwen Image Edit for Urbanism v1.1 expands local AI editing in ComfyUI with advanced sampling and dual-image workflows. The new Lightning LoRA system improves realism, texture fidelity, and processing speed, enabling fast, privacy-preserving urban scene transformation—entirely offline.